Our research is focused on the use of advanced ontological models as a foundation for computerized Clinical Decision Support (CDS) systems that link hospitalized patient data routinely captured in electronic medical records (EMRs) with medical knowledge to effectively influence timely awareness and treatment choices by clinicians. Our phase 1 goal is to demonstrate how our advanced CDS technology has the potential to improve preventable mortality outcomes associated with sepsis in ICU patients. We distinguish two types of CDS algorithms available today for detecting and/or predicting sepsis using EMR data: 1) evidence-based knowledge-driven detection algorithms; and 2) data-driven predictive algorithms based on machine learning (ML) techniques. Recent studies indicate currently available CDS tools do not reduce risk of death in hospitalized patients. We believe this may be because diseases such as sepsis are time-sensitive, complex syndromes and also due to the challenges of computerized reuse of unstructured EMR data. Our sepsis ontology models this complexity to: a) provide enhanced knowledge-driven sepsis risk- stratified cohort classifications that help guide interventions; b) support accurate natural language processing (NLP) of free text clinical notes to enhance real-time sepsis risk detection; and c) improve the accuracy of data- driven prediction models when used in conjunction with ML training algorithms. Our CDS is based on proprietary cluster computing technology we call VFusion designed to efficiently deal with the generation and practical use of large, application domain-specific ontologies. Our sepsis ontology employs a family of upper level ontologies, combined with granular evidence-based domain ontologies, configurable rule sets (e.g. first order logic-based), and required components of reference terminologies. Our research will use openly available ICU patient data to establish statistical detection/prediction performance metrics using this ontology in 2 modes of use: 1) as a knowledge-based screening tool to detect subtle signs of sepsis in individualized hospitalized patients; 2) used in conjunction with ML to improve data-driven predictive performance. We will measure specificity/sensitivity, and positive/negative predictive power of our hybrid ontology-based technology to demonstrate dramatically improved performance over existing CDS algorithms. In Phase II we plan a retrospective demonstration with a much larger sample of patients to include non-ICU patients in collaboration with a major healthcare system. Our product vision is an early inpatient sepsis detection algorithm with high accuracy embodied as a plug-in application compatible with any modern EMR platform in use at a client hospital effective in both ICU and non-ICU care settings.